Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models. - Gideon Mann; Ryan T. McDonald; Mehryar Mohri; Nathan Silberman; Dan Walker (NIPS, 2009)

Case for Automated Detection of Diabetic Retinopathy. - Nathan Silberman; Kristy Ahrlich; Rob Fergus; Lakshminarayanan Subramanian (AAAI Spring Symposium: Artificial Intelligence for Development, 2010)

Indoor scene segmentation using a structured light sensor. - Nathan Silberman; Rob Fergus (ICCV Workshops, 2011)

Indoor Segmentation and Support Inference from RGBD Images. - Nathan Silberman; Derek Hoiem; Pushmeet Kohli; Rob Fergus (ECCV (5), 2012)

Instance Segmentation of Indoor Scenes Using a Coverage Loss. - Nathan Silberman; David A. Sontag; Rob Fergus (ECCV (1), 2014)

A Contour Completion Model for Augmenting Surface Reconstructions. - Nathan Silberman; Lior Shapira; Ran Gal; Pushmeet Kohli (ECCV (3), 2014)

Im2Calories: Towards an Automated Mobile Vision Food Diary. - Austin Myers; Nick Johnston; Vivek Rathod; Anoop Korattikara; Alexander N. Gorban; Nathan Silberman; Sergio Guadarrama; George Papandreou; Jonathan Huang; Kevin Murphy 002 (ICCV, 2015)

Instance Segmentation of RGBD Scenes. - Nathan Silberman (2015)

Domain Separation Networks. - Konstantinos Bousmalis; George Trigeorgis; Nathan Silberman; Dilip Krishnan; Dumitru Erhan (NIPS, 2016)

The Devil is in the Decoder. - Zbigniew Wojna; Jasper R. R. Uijlings; Sergio Guadarrama; Nathan Silberman; Liang-Chieh Chen; Alireza Fathi; Vittorio Ferrari (BMVC, 2017)

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. - Konstantinos Bousmalis; Nathan Silberman; David Dohan; Dumitru Erhan; Dilip Krishnan (CVPR, 2017)

ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations. - Pouya Samangouei; Ardavan Saeedi; Liam Nakagawa; Nathan Silberman (ECCV (10), 2018)

Learning From Noisy Labels by Regularized Estimation of Annotator Confusion. - Ryutaro Tanno; Ardavan Saeedi; Swami Sankaranarayanan; Daniel C. Alexander; Nathan Silberman (CVPR, 2019)

The Devil is in the Decoder: Classification, Regression and GANs. - Zbigniew Wojna; Vittorio Ferrari; Sergio Guadarrama; Nathan Silberman; Liang-Chieh Chen; Alireza Fathi; Jasper R. R. Uijlings (Int. J. Comput. Vis., 2019)

Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth. - Igor Lovchinsky; Alon Daks; Israel Malkin; Pouya Samangouei; Ardavan Saeedi; Yang Liu; Swami Sankaranarayanan; Tomer Gafner; Ben Sternlieb; Patrick Maher; Nathan Silberman (ICLR, 2020)